Determinants of V+O2peak Changes After Aerobic Training in Coronary Heart Disease Patients
Bibliographic record
Abstract
Abstract This study aimed to highlight the ventilatory and circulatory determinants of changes in ˙VO2peak after exercise-based cardiac rehabilitation (ECR) in patients with coronary heart disease (CHD). Eighty-two CHD patients performed, before and after a 3-month ECR, a cardiopulmonary exercise testing (CPET) on a bike with gas exchanges measurements (˙VO2peak, minute ventilation, i. e., ˙VE), and cardiac output (Q˙c). The arteriovenous difference in O2 (C(a-v¯)O2) and the alveolar capillary gradient in O2 (PAi-aO2) were calculated using Fick’s laws. Oxygen uptake efficiency slope (OUES) was calculated. A 5.0% cut off was applied for differentiating non- (NR: ˙VO2<0.0%), low (LR: 0.0≤ ∆˙VO2<5.0%), moderate (MR: 5.0≤∆˙VO2 < 10.0%), and high responders (HR: ∆˙VO2≥10.0%) to ECR. A total of 44% of patients were HR (n=36), 20% MR (n=16), 23% LR (n=19), and 13% NR (n=11). For HR, the ˙VO2peak increase (p<0.01) was associated with increases in ˙VE (+12.8±13.0 L/min, p<0.01), (+1.0±0.9 L/min, p<0.01), and C(a-v¯)O2 (+2.3±2.5 mLO2/100 mL, p<0.01). MR patients were characterized by+6.7±19.7 L/min increase in ˙VE (p=0.04) and+0.7±1.0 L/min of Q˙c (p<0.01). ECR induced decreases in ˙VE (p=0.04) and C(a-v¯)O2 (p<0.01) and a Q˙c increase in LR and NR patients (p<0.01). Peripheral and ventilatory responses more than central adaptations could be responsible for the ˙VO2peak change with ECR in CHD patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".